A Bayesian network model for the probabilistic safety assessment of offshore wind decommissioning

Abstract

With increasing the number of wind turbines approaching the end of their service life, it has become crucial for businesses to understand and assess safety and security issues related to the decommissioning phase of wind farm asset lifecycle. This paper aims to develop, for the first time, a Bayesian Network (BN) model for the safety assessment of offshore wind farm decommissioning operations. The most critical safety incidents are identified and their corresponding risk-influencing factors (RIF) are determined. The impacts of human errors as well as procedural and mechanical/electrical failures on the safety and efficiency of decommissioning operations are thoroughly analysed. The findings of the study revealed that the most critical RIFs during offshore wind decommissioning operations include: visibility, crew fatigue, number of personnel per operation, proper safety procedures, crane integrity, number of lifts available in the wind farm, inspection frequency, as well as equipment design.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 11 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.